Abstract
The monitoring of sleep patterns without patient’s inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording the electroencephalogram (ear-EEG). The selected features for sleep pattern classification from a single ear-EEG channel include the spectral edge frequency and multi-scale fuzzy entropy, a structural complexity feature. In this preliminary study, the manually scored hypnograms from simultaneous scalp-EEG and ear-EEG recordings of four subjects are used as labels for two analysis scenarios: 1) classification of ear-EEG hypnogram labels from ear-EEG recordings; and 2) prediction of scalp-EEG hypnogram labels from ear-EEG recordings. We consider both 2-class and 4-class sleep scoring, with the achieved accuracies ranging from 78.5% to 95.2% for ear-EEG labels predicted from ear-EEG, and 76.8% to 91.8% for scalp-EEG labels predicted from ear-EEG. The corresponding Kappa coefficients range from 0.64 to 0.83 for Scenario 1, and indicate substantial to almost perfect agreement, while for Scenario 2 the range of 0.65–0.80 indicates substantial agreement, thus further supporting the feasibility of in-ear sensing for sleep monitoring in the community.
Highlights
Sleep is an essential process in the internal control of the state of body and mind and its quality is strongly linked with a number of cognitive and health issues, such as stress, depression and memory [1]
We first evaluated the agreement between the hypnogram scored based on ear EEG sensor (ear-EEG) channels and the predicted label based on extracted features from the in-ear EEG channel ear channel 1 (EL1)
SCENARIO2: SLEEP STAGE CLASSIFICATION FROM EAR-EEG AGAINST THE MANUALLY SCORED HYPNOGRAM BASED ON SCALP-EEG We evaluated the agreement between the hypnogram scored based on scalp-EEG channels and the predicted label based on extracted features from the in-ear EEG channel EL1
Summary
Sleep is an essential process in the internal control of the state of body and mind and its quality is strongly linked with a number of cognitive and health issues, such as stress, depression and memory [1]. While the PSG is able to faithfully reflect human sleep patterns, both the recording and scoring process are expensive as this involves an overnight stay in a specialised clinic and time-consuming manual scoring by a medically trained person. Consumer technologies are becoming increasingly popular for the self-monitoring of sleep [2], and include both mobile apps and wearable devices. While such technologies aim to assess ‘sleep quality’ and are affordable, these are typically not direct measures of neural activity, and instead measure indirect surrogates of sleep such as limb movement [3]
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